Sequential model-based segmentation and recognition of image structures driven by visual features and spatial relations

Fouquier, Geoffroy; Atif, Jamal; Bloch, Isabelle

doi:10.1016/j.cviu.2011.09.004

Cited by 36 publications

(22 citation statements)

References 22 publications

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“…With few exceptions [10], multiobject generalizations of this approach do not, in general, have such optimality properties due to the NP-hardness of the underlying problem, but some algorithms [11] are nevertheless capable of finding a local solution optimal within a known factor of the global optimum. Simultaneous multiobject segmentation approaches, on the other hand, are superior to their sequential counterparts in that they raise questions neither on the best segmentation sequence to follow nor on how to avoid the propagation of errors on individual segmentations [2].…”

Section: A Graph Cut In Segmentation and Its Complexitymentioning

confidence: 99%

“…Intuitively, these relations represent the degree of influence of an organ's surface upon another. A generic, graph-based scene model representing spatial information explicitly is also used by [2] in the problem of segmenting multiple subcortical structures in brain MR images. Fuzzy representations of distance, adjacency and spatial directionality relations are used to account for model imprecisions and variabilities.…”

Section: B Prior Information In Graph Cut Segmentationmentioning

confidence: 99%

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Shortest-Path Constraints for 3D Multiobject Semiautomatic Segmentation Via Clustering and Graph Cut

Kéchichian

Valette

Desvignes

et al. 2013

IEEE Trans. on Image Process.

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To cite this version:Razmig Kéchichian, Sébastien Valette, Michel Desvignes, Rémy Prost. Shortest-path constraints for 3D multiobject semiautomatic segmentation via clustering and Graph Cut. IEEE Transactions on Image Processing, Institute of Electrical and Electronics Engineers, 2013, 22 (11) Abstract-We derive shortest-path constraints from graph models of structure adjacency relations and introduce them in a joint centroidal Voronoi image clustering and Graph Cut multiobject semiautomatic segmentation framework. The vicinity prior model thus defined is a piecewise-constant model incurring multiple levels of penalization capturing the spatial configuration of structures in multiobject segmentation. Qualitative and quantitative analysis and comparison with a Potts prior-based approach and our previous contribution on synthetic, simulated and real medical images show that the vicinity prior allows for the correct segmentation of distinct structures having identical intensity profiles and improves the precision of segmentation boundary placement while being fairly robust to clustering resolution. The clustering approach we take to simplify images prior to segmentation strikes a good balance between boundary adaptivity and cluster compactness criteria furthermore allowing to control the trade-off. Compared to a direct application of segmentation on voxels, the clustering step improves the overall runtime and memory footprint of the segmentation process up to an order of magnitude without compromising the quality of the result.

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Section: A Graph Cut In Segmentation and Its Complexitymentioning

confidence: 99%

Section: B Prior Information In Graph Cut Segmentationmentioning

confidence: 99%

Shortest-Path Constraints for 3D Multiobject Semiautomatic Segmentation Via Clustering and Graph Cut

Kéchichian

Valette

Desvignes

et al. 2013

IEEE Trans. on Image Process.

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“…For a particular class of cost functions which frequently arises in segmentation applications [16], these methods produce provably good approximate solutions in multiobject [5] and global optima in single-object segmentation. In addition, simultaneous multiobject segmentation approaches are superior to their sequential counterparts in that they raise questions neither on the best segmentation sequence to follow nor on how to avoid the propagation of errors of individual segmentations [9].…”

mentioning

confidence: 99%

“…It is impossible to identify and segment such structures automatically on the basis of intensity information only. Hence, most advanced segmentation methods exploit some form of prior information on structure location [12,19,27] or interrelations [9,14,23,25] to achieve greater robustness and precision. Hierarchical approaches to segmentation [23,25,32] rely on hierarchical organizations of prior information and algorithms that proceed in a coarse-to-fine manner according to anatomical level of detail.…”

mentioning

confidence: 99%

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Automatic Multiorgan Segmentation Using Hierarchically Registered Probabilistic Atlases

Kéchichian

Valette

Desvignes

2017

Cloud-Based Benchmarking of Medical Image Analysis

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We propose a generic method for the automatic multiple-organ segmentation of 3D images based on a multilabel graph cut optimization approach which uses location likelihood of organs and prior information of spatial relationships between them. The latter is derived from shortest-path constraints defined on the adjacency graph of structures and the former is defined by probabilistic atlases learned from a training dataset. Organ atlases are mapped to the image by a fast (2+1)D hierarchical registration method based on SURF keypoints. Registered atlases are also used to derive organ intensity likelihoods. Prior and likelihood models are then introduced in a joint centroidal Voronoi image clustering and graph cut multiobject segmentation framework. Qualitative and quantitative evaluation has been performed on contrast-enhanced CT and MR images from the VISCERAL dataset.

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Fuzzy methods in medical imaging

Bloch

2015

Handbook of Biomedical Imaging

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Fuzzy sets theory is of great interest in medical image processing, for dealing with imprecise information and knowledge. It provides a consistent mathematical framework for knowledge representation, information modeling at different levels, fusion of heterogeneous information, reasoning and decision making. In this chapter, we provide an overview of the potential of this theory in medical imaging, in particular for classification, segmentation and recognition of anatomical and pathological structures.

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Sequential model-based segmentation and recognition of image structures driven by visual features and spatial relations

Cited by 36 publications

References 22 publications

Shortest-Path Constraints for 3D Multiobject Semiautomatic Segmentation Via Clustering and Graph Cut

Shortest-Path Constraints for 3D Multiobject Semiautomatic Segmentation Via Clustering and Graph Cut

Automatic Multiorgan Segmentation Using Hierarchically Registered Probabilistic Atlases

Fuzzy methods in medical imaging

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